In principle, NMR should be ideally suited for fast structural/functional screening of novel genomic modules. In practice, the analysis of protein NMR structural data is hampered by the assignment of the spectral signals to the individual spins (1H, 13C, 15N, etc.). Much effort is being devoted to expedite the assignment process. Progress stems mainly from the design of increasingly powerful heteronuclear multidimensional NMR experiments, interactive computer-based sorting of spin-spin scalar and Overhauser connectivities, and refinement from spin-spin dipolar couplings. In order to minimize the assignment bottleneck, we have developed a protocol, CLOUDS, that relies on a relaxation matrix-based analysis of nuclear Overhauser data (2D-NOESY spectra). A practical advantage of CLOUDS is that it leads to a computed location of unassigned distributed spins. A "structure" free of covalent linkages --in fact, a cloud of protons-- is thus obtained which represents the spatial location of the H-atoms relative to each other. By overlapping a large number (>1000) of such clouds, a family of clouds (foc) in 3D Cartesian space is generated that represents an effective proton density distribution. By fitting the linear polypeptide sequence to the foc proton density, a structure can be derived that is comparable to those obtained via traditional NMR methods. Here, we propose to further develop CLOUDS and enhance its capabilities for high throughput NMR structure elucidation. We propose to develop SPI, a program which automatically groups resonances into spin systems, based on 2D homonuclear and 3D 15N-edited data. We also propose a novel computational protocol, BACUS, for identifying resonances grouped according to a significant fraction of NOEs. BACUS does not require resonance assignments, thus addressing a main drawback to the NOE-based "direct" methods. BACUS is not related to any of the reported NOESY assignment protocols and, in principle, is applicable, not only for CLOUDS, but also within a conventional structural elucidation strategy. Finally, we propose to expand the protocol by incorporating other refinement criteria, such as dihedral angle constraints, residual dipolar couplings, anti-distance constraints, etc., to speed up the procedure and improve the CLOUDS- derived structures. A main objective of this project is the development of a robust computational protocol to obtain protein folds with minimal amounts of NMR experimental data. [unreadable] [unreadable]